Table Of ContentVideo-based step measurement in sport and daily living.
DUNN, Marcus David.
Available from Sheffield Hallam University Research Archive (SHURA) at:
http://shura.shu.ac.uk/20193/
This document is the author deposited version. You are advised to consult the
publisher's version if you wish to cite from it.
Published version
DUNN, Marcus David. (2014). Video-based step measurement in sport and daily
living. Doctoral, Sheffield Hallam University (United Kingdom)..
Copyright and re-use policy
See http://shura.shu.ac.uk/information.html
Sheffield Hallam University Research Archive
http://shura.shu.ac.uk
Learning ana 11 services
Collegiate Learning Centre
Collegiate Crescent Campus
Sheffield S1G2BP
102 044 7 7 1 0
REFERENCE
ProQuest Number: 10700838
All rights reserved
INFORMATION TO ALL USERS
The quality of this reproduction is dependent upon the quality of the copy submitted.
In the unlikely event that the author did not send a com ple te manuscript
and there are missing pages, these will be noted. Also, if material had to be removed,
a note will indicate the deletion.
uest
ProQuest 10700838
Published by ProQuest LLC(2017). Copyright of the Dissertation is held by the Author.
All rights reserved.
This work is protected against unauthorized copying under Title 17, United States Code
Microform Edition © ProQuest LLC.
ProQuest LLC.
789 East Eisenhower Parkway
P.O. Box 1346
Ann Arbor, Ml 48106- 1346
Video-based step measurement in sport and daily living
Marcus David Dunn
A thesis submitted in partial fulfilment of the requirements of
Sheffield Hallam University
for the degree of Doctor of Philosophy
March 2014
Collaborating organisation: International Tennis Federation
Abstract
Current knowledge of tennis player-surface interactions with different court surfaces is
limited. The measurement of player step and movement strategy would aid the
understanding of tennis player-surface interaction. However, this has not yet been
performed: no readily available motion analysis tool is capable of measuring spatio-
temporal parameters of gait during match-play tennis. The purpose of this project was to
develop, validate and use a motion analysis tool designed to measure player location
and foot-surface contacts during match-play tennis.
Single camera video footage, obtained from the 2011 Roland Garros Qualifying
Tournament, was manually digitised to characterise step and movement strategy during
men's and women's forehand groundstrokes. Player movements were consistent with
previous notational analyses; however gender differences were highlighted for step
frequency. Initial findings were limited by manual analysis, e.g. manual digitising
subjectivity and low sample size: an objective and automated system was required.
A markerless, view-independent, foot-surface contact identification (FSCi) algorithm
was developed. The FSCi algorithm identifies foot-surface contacts in image sequences
of gait by quantifying the motion of each foot. The algorithm was validated using
standard colour image sequences of walking and running obtained from four unique
camera perspectives: output data were compared to three-dimensional motion analysis.
The FSCi algorithm identified data for 1243 of 1248 foot-surface contacts; root-mean-
square error (RMSE) was 52.2 and 103.4 mm for shod walking and running
respectively (all camera perspectives). Findings demonstrated that the FSCi algorithm
measured basic, spatio-temporal parameters of walking and running, e.g. step length and
step time, without interfering with the activity being observed. Furthermore, analyses
were independent of camera view.
Video footage obtained from the 2011 ATP World Tour Finals was used to develop a
combined player tracking and foot-surface contact identification (PT-FSCi) algorithm.
Furthermore, a graphical user interface was developed. The PT-FSCi algorithm was
used to analyse twenty match-play tennis rallies: output data were compared to manual
digitising. The PT-FSCi algorithm tracked player position and identified data for 832 of
890 foot-surface contacts during match-play tennis. RMSE for player position and foot-
surface contacts was 232.9 and 121.9 mm respectively. The calculation of step
parameters required manual intervention: this reflected the multi-directional nature of
tennis. This represents a limitation to the current algorithm however the segmentation of
player movement phases to allow the automatic calculation of step parameters.
The analysis of this data indicated that top ranked tennis players can win rallies using
movement strategies previously considered to be defensive. Furthermore, step length
data indicated that shorter step lengths formed the majority of step strategy. The largest
25% of steps were observed behind the baseline, aligned with deuce and advantage
court sidelines. This reflected lunging and turning manoeuvres at lateral extremes of
player movement.
The single camera system that has resulted from this project will enable the
International Tennis Federation to characterise player step and movement strategy
during match-play tennis. This will allow a more informed approach to player-surface
interaction research. Furthermore, the system has potential to be used for different
applications, ranging from sport to surveillance.
ii
Acknowledgements
I would like to thank Dr Simon Goodwill, Dr Jon Wheat and Professor Steve Haake
for their continued support and advice throughout this project. Their experience has
proved both insightful and motivating. My thanks are also extended to everyone
within the Centre for Sports Engineering Research at Sheffield Hallam University.
I am very grateful to the International Tennis Federation for their sponsorship of this
project. In particular, I would like to thank Dr Stuart Miller, Jamie Capel-Davies and
James Spurr for their invaluable assistance and continued support for this project.
Finally, I would like to thank Dr Karl Cooke of British Swimming (formerly the
Lawn Tennis Association), for his assistance in obtaining video footage of elite,
match-play tennis.
Contents
Abstract ii
Acknowledgements iii
Contents iv
List of Figures x
List of Tables xxi
Nomenclature xxiii
1 Introduction 1
1.1. Motivation for research 1
1.2. Aim and objectives 3
2 Literature review 5
2.1. Introduction 5
2.1.1. History of tennis 5
2.1.2. Science and tennis 7
2.1.3. Tennis player movement and injury 9
2.1.4. Conclusion 12
2.2. Motion analysis techniques 13
2.2.1. Non-intrusive motion analysis technologies in tennis 14
2.2.2. Non-intrusive player motion analysis 15
2.2.3. Sport stadia 20
2.2.4. Conclusion 25
2.3. Gait measurement techniques 26
2.3.1. Holistic approaches 26
2.3.2. Model-based approaches 28
2.3.3. Model-based heel strike detection 31
2.2.4. Background modelling 33
2.2.5. Conclusion 35
2.4. Chapter findings 36
2.4.1. Player tracking 36
2.4.2. Heel strike extraction 37
2.4.3. Conclusion 37
3 Player step and movement characterisation at the 2011 Roland Garros 38
Qualifying Tournament
3.1. Introduction 38
3.2. Aim and obj ectives 3 8
iv
3.3. Measuring player step and movement strategy at the 2011 39
Roland Garros Qualifying Tournament
3.3.1. Single camera calibration 3 9
3.3.2. Footage collection in sport stadia 41
3.3.3. Elevated calibration plane 42
3.3.4. Player step and movement definitions 43
3.3.5. Analysis software 44
3.4. Results 46
3.5. Discussion 48
3.5.1. Rally characterisation 48
3.5.2. Filming and analysis 49
3.6. Conclusion 50
4 Single camera position reconstruction 52
4.1. Introduction 52
4.2. Aim and objectives 53
4.3. Monocular photogrammetry 53
4.3.1. Camera model 53
4.3.2. Camera-plane model 57
4.3.3. Position reconstruction 59
4.4. Ground plane position reconstruction assessment 61
4.4.1. Competitive tennis environments: data collection and 61
modelling
4.5. Results 68
4.6. Discussion 72
4.7. Conclusion 75
5 Single camera player position reconstruction using an elevated 77
calibration plane
5.1. Introduction 77
5.2. Aim and objectives 77
5.3. Out-of-plane error 78
5.4. Simulating sport stadia camera views 79
5.4.1. Player position reconstruction 84
5.5. Results 86
5.6. Discussion 93
5.7. Conclusion 96
v
6 An automatic technique for identifying foot-surface contacts 98
6.1. Introduction 98
6.2. Aim and objectives 98
6.3. Vision-based foot-surface contact identification 99
6.3.1. Single camera methods 99
6.3.2. Logical image processing 100
6.3.3. S ynthetic walking data 103
6.3.4. Foot-region segmentation 106
6.3.5. Foot-region inter-frame motion 108
6.4. Development of an algorithm to measure foot-surface 111
contacts
6.4.1. Colour image collection 112
6.4.2. Background segmentation 113
6.4.3. Shadow removal 114
6.4.4. Inter-frame motion 117
6.4.5. Foot segmentation 118
6.4.6. Foot-surface contact threshold 121
6.4.7. Geometric rules 126
6.5. Application to walking and running 128
6.6. Conclusion 133
7 Validation of an automatic technique for identifying foot-surface 134
contacts in walking and running
7.1. Introduction 13 4
7.2. Aim and objectives 134
7.3. Laboratory validation study 135
7.3.1. Participants and procedures 135
7.3.2. Experimental setup 135
7.3.3. Criterion data treatment 138
7.3.4. FSCi algorithm data treatment 140
7.3.5. Reconstruction plane elevation 141
7.3.6. Data analysis 142
7.4. Results 144
7.4.1. Reconstruction plane elevation 145
7.4.2. Foot-surface contacts 147
7.4.3. Step parameters and reconstruction plane elevation 154
vi
7.5. Discussion 157
7.5.1. Analysis time and identification rate 157
7.5.2. Foot-surface contact time 159
7.5.3. Foot-surface contact position 162
7.5.4. Step parameters 165
7.5.5. Reconstruction plane elevation 166
7.6. Conclusion 166
8 A semi-automatic technique for player tracking and foot-surface 168
identification at the 2011 ATP World Tour Finals
8.1. Introduction 168
8.2. Aim and objectives 169
8.3. Development of a semi-automatic technique to identify player 169
position and foot-surface contacts
8.3.1. Image collection and intrinsic camera calibration 169
8.3.2. Player segmentation 173
8.3.3. Court line and shadow processing 174
8.3.4. Player tracking windows 178
8.3.5. Assessing player tracking 180
8.3.6. Foot-surface contact identification 181
8.3.7. Extrinsic camera calibration and position 182
reconstruction
8.3.8. Graphical user interface 183
8.4. Application to match play tennis 187
8.5. Conclusion 192
9 Validation of a semi-automatic technique for player tracking and 193
foot-surface contact identification at the 2011 ATP World Tour
Finals
9.1. Introduction 193
9.2. Aim and objectives 193
9.3. Match play tennis validation study 193
9.3.1. Camera calibration parameters 194
9.3.2. PT-FSCi tennis rally analysis 196
9.3.3. Manual tennis rally analysis 197
9.3.4. Data analysis 198
9.4. Results 199
vii
